Screening rates for colorectal cancer in Canada: a cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Implementation of population-based colorectal cancer (CRC) screening programs should reduce disparities in participation in CRC screening. We estimated CRC screening rates in 2012 in Canada and assessed predictors of screening in provinces with and without well-established population-based screening programs. METHODS: We used data from the Canadian Community Health Survey for 2012 to calculate the prevalence of up-to-date CRC screening, defined as fecal occult blood testing (FOBT) within 2 years before the survey or flexible sigmoidoscopy or colonoscopy within 10 years before the survey, or both. Weighted proportions of individuals with up-to-date screening were calculated and logistic regression analysis performed to assess predictors of up-to-date CRC screening, including differences in participation by income level. RESULTS: The prevalence of up-to-date CRC screening among people 50-74 years of age in 2012 was 55.2%, ranging from 41.3% in the territories to 67.2% in the province of Manitoba. The rate for sigmoidoscopy or colonoscopy was 37.2% (highest in Ontario, at 43.3%), and for FOBT it was 30.1% (highest in Manitoba, at 51.7%). About 41% of those who had an FOBT also had a sigmoidoscopy or colonoscopy. Individuals in the highest income group were more likely than those in lower-income groups to be up to date with CRC screening, even in provinces with well-established population-based screening programs. INTERPRETATION: More than half of Canadians were up to date with CRC screening in 2012, but there were large differences among provinces. Differences by income group in provinces with population-based screening programs need particular attention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".